






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCamelCase        =				logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")

@dataclass
class        lowerCAmelCase_				:







				"""simple docstring"""




				_snake_case				:   Optional[str]        =			field(
				    default="""cifar10"""						,      metadata={"""help""": """Name of a dataset from the datasets package"""}	)
				_snake_case				:   Optional[str]        =			field(
				    default=lowercase						,      metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}	)
				_snake_case				:   Optional[str]        =			field(
				    default=lowercase						,      metadata={"""help""": """The column name of the images in the files."""}	)
				_snake_case				:   Optional[str]        =			field(default=lowercase						,      metadata={"""help""": """A folder containing the training data."""}	)
				_snake_case				:   Optional[str]        =			field(default=lowercase						,      metadata={"""help""": """A folder containing the validation data."""}	)
				_snake_case				:   Optional[float]        =			field(
				    default=0.15						,      metadata={"""help""": """Percent to split off of train for validation."""}	)
				_snake_case				:   Optional[int]        =			field(
				    default=lowercase						,      metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of training examples to this """
				            """value if set."""
				        )
				    }						,      )
				_snake_case				:   Optional[int]        =			field(
				    default=lowercase						,      metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
				            """value if set."""
				        )
				    }						,      )







				def 				__a   ( self     :List[str]     ):
					UpperCamelCase__ :Optional[Any]						=      {}
					if self.train_dir is not None:
						UpperCamelCase__ :int						=      self.train_dir
					if self.validation_dir is not None:
						UpperCamelCase__ :List[str]						=      self.validation_dir
					UpperCamelCase__ :Optional[int]						=      data_files if data_files else None

@dataclass
class        lowerCAmelCase_				:







				"""simple docstring"""




				_snake_case				:   str        =			field(
				    default=lowercase						,      metadata={
				        """help""": (
				            """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
				        )
				    }						,      )
				_snake_case				:   Optional[str]        =			field(
				    default=lowercase						,      metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}	)
				_snake_case				:   Optional[str]        =			field(
				    default=lowercase						,      metadata={
				        """help""": (
				            """Override some existing default config settings when a model is trained from scratch. Example: """
				            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
				        )
				    }						,      )
				_snake_case				:   Optional[str]        =			field(
				    default=lowercase						,      metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}	)
				_snake_case				:   str        =			field(
				    default="""main"""						,      metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}						,      )
				_snake_case				:   str        =			field(default=lowercase						,      metadata={"""help""": """Name or path of preprocessor config."""}	)
				_snake_case				:   bool        =			field(
				    default=lowercase						,      metadata={
				        """help""": (
				            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
				            """with private models)."""
				        )
				    }						,      )
				_snake_case				:   float        =			field(
				    default=0.75						,      metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}	)
				_snake_case				:   bool        =			field(
				    default=lowercase						,      metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}	)

@dataclass
class        lowerCAmelCase_				(      lowercase	):







				"""simple docstring"""




				_snake_case				:   float        =			field(
				    default=1e-3						,      metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}	)




def 				A		(					lowercase__  :  Union[str, Any]  )		-> Dict:
	UpperCamelCase__ :Union[str, Any]						=      torch.stack([example["""pixel_values"""] for example in examples]  )
	return {"pixel_values": pixel_values}




def 				A		(					)		-> Optional[int]:
	# See all possible arguments in src/transformers/training_args.py
	# or by passing the --help flag to this script.
	# We now keep distinct sets of args, for a cleaner separation of concerns.

	UpperCamelCase__ :Optional[int]						=      HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)  )
	if len(sys.argv  ) == 2 and sys.argv[1].endswith(""".json"""  ):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :str						=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
	else:
		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :Union[str, Any]						=      parser.parse_args_into_dataclasses()

	# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
	# information sent is the one passed as arguments along with your Python/PyTorch versions.
	send_example_telemetry("""run_mae""" ,		lowercase__ ,		lowercase__  )

	# Setup logging
	logging.basicConfig(
	    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,		datefmt="""%m/%d/%Y %H:%M:%S""" ,		handlers=[logging.StreamHandler(sys.stdout  )] ,		)

	if training_args.should_log:
		# The default of training_args.log_level is passive, so we set log level at info here to have that default.
		transformers.utils.logging.set_verbosity_info()

	UpperCamelCase__ :List[str]						=      training_args.get_process_log_level()
	logger.setLevel(lowercase__  )
	transformers.utils.logging.set_verbosity(lowercase__  )
	transformers.utils.logging.enable_default_handler()
	transformers.utils.logging.enable_explicit_format()

	# Log on each process the small summary:
	logger.warning(
	    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
	    + f"""distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}"""  )
	logger.info(f"""Training/evaluation parameters {training_args}"""  )

	# Detecting last checkpoint.
	UpperCamelCase__ :Union[str, Any]						=      None
	if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
		UpperCamelCase__ :List[str]						=      get_last_checkpoint(training_args.output_dir  )
		if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
			raise ValueError(
			    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
			    """Use --overwrite_output_dir to overcome."""  )
		elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
			logger.info(
			    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
			    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""  )

    # Initialize our dataset.
	UpperCamelCase__ :Tuple						=      load_dataset(
	    data_args.dataset_name ,		data_args.dataset_config_name ,		data_files=data_args.data_files ,		cache_dir=model_args.cache_dir ,		use_auth_token=True if model_args.use_auth_token else None ,		)

	# If we don't have a validation split, split off a percentage of train as validation.
	UpperCamelCase__ :int						=      None if """validation""" in ds.keys() else data_args.train_val_split
	if isinstance(data_args.train_val_split ,		lowercase__  ) and data_args.train_val_split > 0.0:
		UpperCamelCase__ :Optional[Any]						=      ds["""train"""].train_test_split(data_args.train_val_split  )
		UpperCamelCase__ :Union[str, Any]						=      split["""train"""]
		UpperCamelCase__ :Any						=      split["""test"""]

	# Load pretrained model and image processor
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.
	UpperCamelCase__ :Optional[int]						=      {
	    """cache_dir""": model_args.cache_dir,
	    """revision""": model_args.model_revision,
	    """use_auth_token""": True if model_args.use_auth_token else None,
	}
	if model_args.config_name:
		UpperCamelCase__ :Any						=      ViTMAEConfig.from_pretrained(model_args.config_name ,		**lowercase__  )
	elif model_args.model_name_or_path:
		UpperCamelCase__ :Union[str, Any]						=      ViTMAEConfig.from_pretrained(model_args.model_name_or_path ,		**lowercase__  )
	else:
		UpperCamelCase__ :Optional[Any]						=      ViTMAEConfig()
		logger.warning("""You are instantiating a new config instance from scratch."""  )
		if model_args.config_overrides is not None:
			logger.info(f"""Overriding config: {model_args.config_overrides}"""  )
			config.update_from_string(model_args.config_overrides  )
			logger.info(f"""New config: {config}"""  )

    # adapt config
	config.update(
	    {
	        """mask_ratio""": model_args.mask_ratio,
	        """norm_pix_loss""": model_args.norm_pix_loss,
	    }  )

	# create image processor
	if model_args.image_processor_name:
		UpperCamelCase__ :str						=      ViTImageProcessor.from_pretrained(model_args.image_processor_name ,		**lowercase__  )
	elif model_args.model_name_or_path:
		UpperCamelCase__ :Dict						=      ViTImageProcessor.from_pretrained(model_args.model_name_or_path ,		**lowercase__  )
	else:
		UpperCamelCase__ :Tuple						=      ViTImageProcessor()

	# create model
	if model_args.model_name_or_path:
		UpperCamelCase__ :Any						=      ViTMAEForPreTraining.from_pretrained(
		    model_args.model_name_or_path ,		from_tf=bool(""".ckpt""" in model_args.model_name_or_path  ) ,		config=lowercase__ ,		cache_dir=model_args.cache_dir ,		revision=model_args.model_revision ,		use_auth_token=True if model_args.use_auth_token else None ,		)
	else:
		logger.info("""Training new model from scratch"""  )
		UpperCamelCase__ :Optional[int]						=      ViTMAEForPreTraining(lowercase__  )

	if training_args.do_train:
		UpperCamelCase__ :Optional[Any]						=      ds["""train"""].column_names
	else:
		UpperCamelCase__ :Union[str, Any]						=      ds["""validation"""].column_names

	if data_args.image_column_name is not None:
		UpperCamelCase__ :Union[str, Any]						=      data_args.image_column_name
	elif "image" in column_names:
		UpperCamelCase__ :Optional[Any]						=      """image"""
	elif "img" in column_names:
		UpperCamelCase__ :List[str]						=      """img"""
	else:
		UpperCamelCase__ :List[Any]						=      column_names[0]

	# transformations as done in original MAE paper
	# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
	if "shortest_edge" in image_processor.size:
		UpperCamelCase__ :List[str]						=      image_processor.size["""shortest_edge"""]
	else:
		UpperCamelCase__ :int						=      (image_processor.size["""height"""], image_processor.size["""width"""])
	UpperCamelCase__ :Any						=      Compose(
	    [
	        Lambda(lambda lowercase__  : img.convert("""RGB"""  ) if img.mode != "RGB" else img  ),
	        RandomResizedCrop(lowercase__ ,		scale=(0.2, 1.0) ,		interpolation=InterpolationMode.BICUBIC  ),
	        RandomHorizontalFlip(),
	        ToTensor(),
	        Normalize(mean=image_processor.image_mean ,		std=image_processor.image_std  ),
	    ]  )

	def preprocess_images(lowercase__  :  Tuple  ):
		UpperCamelCase__ :List[Any]						=      [transforms(lowercase__  ) for image in examples[image_column_name]]
		return examples

	if training_args.do_train:
		if "train" not in ds:
			raise ValueError("""--do_train requires a train dataset"""  )
		if data_args.max_train_samples is not None:
			UpperCamelCase__ :Optional[int]						=      ds["""train"""].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
		# Set the training transforms
		ds["train"].set_transform(lowercase__  )

	if training_args.do_eval:
		if "validation" not in ds:
			raise ValueError("""--do_eval requires a validation dataset"""  )
		if data_args.max_eval_samples is not None:
			UpperCamelCase__ :Optional[Any]						=      (
			    ds["""validation"""].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
			)
		# Set the validation transforms
		ds["validation"].set_transform(lowercase__  )

	# Compute absolute learning rate
	UpperCamelCase__ :Tuple						=      (
	    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
	)
	if training_args.base_learning_rate is not None:
		UpperCamelCase__ :Any						=      training_args.base_learning_rate * total_train_batch_size / 256

	# Initialize our trainer
	UpperCamelCase__ :Union[str, Any]						=      Trainer(
	    model=lowercase__ ,		args=lowercase__ ,		train_dataset=ds["""train"""] if training_args.do_train else None ,		eval_dataset=ds["""validation"""] if training_args.do_eval else None ,		tokenizer=lowercase__ ,		data_collator=lowercase__ ,		)

	# Training
	if training_args.do_train:
		UpperCamelCase__ :Any						=      None
		if training_args.resume_from_checkpoint is not None:
			UpperCamelCase__ :int						=      training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			UpperCamelCase__ :Dict						=      last_checkpoint
		UpperCamelCase__ :Union[str, Any]						=      trainer.train(resume_from_checkpoint=lowercase__  )
		trainer.save_model()
		trainer.log_metrics("""train""" ,		train_result.metrics  )
		trainer.save_metrics("""train""" ,		train_result.metrics  )
		trainer.save_state()

	# Evaluation
	if training_args.do_eval:
		UpperCamelCase__ :int						=      trainer.evaluate()
		trainer.log_metrics("""eval""" ,		lowercase__  )
		trainer.save_metrics("""eval""" ,		lowercase__  )

	# Write model card and (optionally) push to hub
	UpperCamelCase__ :Optional[int]						=      {
	    """tasks""": """masked-auto-encoding""",
	    """dataset""": data_args.dataset_name,
	    """tags""": ["""masked-auto-encoding"""],
	}
	if training_args.push_to_hub:
		trainer.push_to_hub(**lowercase__  )
	else:
		trainer.create_model_card(**lowercase__  )




def 				A		(					lowercase__  :  Union[str, Any]  )		-> Dict:
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
							main()







'''simple docstring'''





import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_UpperCamelCase						:						int  =					16
_UpperCamelCase						:						int  =					32
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Optional[Any]:
			return int(x / 2**2_0		)


class  snake_case__      :




					def __enter__(  self  :		List[Any]      )    ->							str:
								gc.collect()
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
								UpperCAmelCase_      :       Dict          =       torch.cuda.memory_allocated()
								return self





					def __exit__(  self  :		Optional[Any]				, *_A  :		Any      )    ->							Any:
								gc.collect()
								torch.cuda.empty_cache()
								UpperCAmelCase_      :       Optional[int]          =       torch.cuda.memory_allocated()
								UpperCAmelCase_      :       int          =       torch.cuda.max_memory_allocated()
								UpperCAmelCase_      :       int          =       bamb(self.end - self.begin      )
								UpperCAmelCase_      :       List[str]          =       bamb(self.peak - self.begin      )
								# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def __UpperCAmelCase   (					A					:			Accelerator			,       A					:			int = 1_6			,       A					:			str = "bert-base-cased"			,       A					:			int = 3_2_0			,       A					:			int = 1_6_0			,       )    ->   Dict:
			UpperCAmelCase_      :       List[str]          =       AutoTokenizer.from_pretrained(A		)
			UpperCAmelCase_      :       List[str]          =       load_dataset(
			    '''glue'''			,       '''mrpc'''			,       split={'''train''': F"train[:{n_train}]", '''validation''': F"validation[:{n_val}]"}		)

			def tokenize_function(A					:			int		):
						# max_length=None => use the model max length (it's actually the default)
						UpperCAmelCase_      :       Optional[int]          =       tokenizer(examples['''sentence1''']			,       examples['''sentence2''']			,       truncation=A			,       max_length=A		)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			UpperCAmelCase_      :       Union[str, Any]          =       datasets.map(
			    A			,       batched=A			,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,       load_from_cache_file=A		)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			UpperCAmelCase_      :       Optional[int]          =       tokenized_datasets.rename_column('''label'''			,       '''labels'''		)

			def collate_fn(A					:			List[Any]		):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(A			,       padding='''max_length'''			,       max_length=1_2_8			,       return_tensors='''pt'''		)
						return tokenizer.pad(A			,       padding='''longest'''			,       return_tensors='''pt'''		)

			# Instantiate dataloaders.
			UpperCAmelCase_      :       Optional[Any]          =       DataLoader(
			    tokenized_datasets['''train''']			,       shuffle=A			,       collate_fn=A			,       batch_size=A		)
			UpperCAmelCase_      :       List[Any]          =       DataLoader(
			    tokenized_datasets['''validation''']			,       shuffle=A			,       collate_fn=A			,       batch_size=A		)

			return train_dataloader, eval_dataloader
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Dict		)    ->   Dict:
			# Initialize accelerator
			UpperCAmelCase_      :       Dict          =       Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			UpperCAmelCase_      :       Optional[int]          =       config['''lr''']
			UpperCAmelCase_      :       List[Any]          =       int(config['''num_epochs''']		)
			UpperCAmelCase_      :       Any          =       int(config['''seed''']		)
			UpperCAmelCase_      :       List[str]          =       int(config['''batch_size''']		)
			UpperCAmelCase_      :       Tuple          =       args.model_name_or_path

			set_seed(A		)
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       get_dataloaders(A			,       A			,       A			,       args.n_train			,       args.n_val		)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			UpperCAmelCase_      :       Optional[Any]          =       AutoModelForSequenceClassification.from_pretrained(A			,       return_dict=A		)

			# Instantiate optimizer
			UpperCAmelCase_      :       str          =       (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			UpperCAmelCase_      :       Optional[Any]          =       optimizer_cls(params=model.parameters()			,       lr=A		)

			if accelerator.state.deepspeed_plugin is not None:
						UpperCAmelCase_      :       Tuple          =       accelerator.state.deepspeed_plugin.deepspeed_config[
						    '''gradient_accumulation_steps'''
						]
			else:
						UpperCAmelCase_      :       Tuple          =       1
			UpperCAmelCase_      :       List[Any]          =       (len(A		) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						UpperCAmelCase_      :       List[str]          =       get_linear_schedule_with_warmup(
						    optimizer=A			,       num_warmup_steps=0			,       num_training_steps=A			,       )
			else:
						UpperCAmelCase_      :       Optional[Any]          =       DummyScheduler(A			,       total_num_steps=A			,       warmup_num_steps=0		)

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       accelerator.prepare(
			    A			,       A			,       A			,       A			,       A		)

			# We need to keep track of how many total steps we have iterated over
			UpperCAmelCase_      :       Optional[Any]          =       0
			# We also need to keep track of the stating epoch so files are named properly
			UpperCAmelCase_      :       List[str]          =       0

			# Now we train the model
			UpperCAmelCase_      :       Optional[Any]          =       {}
			for epoch in range(A			,       A		):
						with TorchTracemalloc() as tracemalloc:
									model.train()
									for step, batch in enumerate(A		):
												UpperCAmelCase_      :       List[Any]          =       model(**A		)
												UpperCAmelCase_      :       List[str]          =       outputs.loss
												UpperCAmelCase_      :       Union[str, Any]          =       loss / gradient_accumulation_steps
												accelerator.backward(A		)
												if step % gradient_accumulation_steps == 0:
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

												overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
						accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin		)		)		)
						accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used		)		)
						accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked		)		)
						accelerator.print(
						    '''Total Peak Memory consumed during the train (max): {}'''.format(
						        tracemalloc.peaked + bamb(tracemalloc.begin		)		)		)
						UpperCAmelCase_      :       List[str]          =       tracemalloc.peaked + bamb(tracemalloc.begin		)
						if args.peak_memory_upper_bound is not None:
									assert (
									    train_total_peak_memory[F"epoch-{epoch}"] <= args.peak_memory_upper_bound
									), "Peak memory usage exceeded the upper bound"

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir			,       '''peak_memory_utilization.json'''		)			,       '''w'''		) as f:
									json.dump(A			,       A		)
def __UpperCAmelCase   (					)    ->   List[Any]:
			UpperCAmelCase_      :       Dict          =       argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''		)
			parser.add_argument(
			    '''--model_name_or_path'''			,       type=A			,       default='''bert-base-cased'''			,       help='''Path to pretrained model or model identifier from huggingface.co/models.'''			,       required=A			,       )
			parser.add_argument(
			    '''--output_dir'''			,       type=A			,       default='''.'''			,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''			,       )
			parser.add_argument(
			    '''--peak_memory_upper_bound'''			,       type=A			,       default=A			,       help='''The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'''			,       )
			parser.add_argument(
			    '''--n_train'''			,       type=A			,       default=3_2_0			,       help='''Number of training examples to use.'''			,       )
			parser.add_argument(
			    '''--n_val'''			,       type=A			,       default=1_6_0			,       help='''Number of validation examples to use.'''			,       )
			parser.add_argument(
			    '''--num_epochs'''			,       type=A			,       default=1			,       help='''Number of train epochs.'''			,       )
			UpperCAmelCase_      :       Union[str, Any]          =       parser.parse_args()
			UpperCAmelCase_      :       List[Any]          =       {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
			training_function(A			,       A		)


if __name__ == "__main__":
						main()


